Identification of ribosome biogenesis genes and subgroups in ischaemic stroke.

Background: Ischaemic stroke is a leading cause of death and severe disability worldwide. Given the importance of protein synthesis in the inflammatory response and neuronal repair and regeneration after stroke, and that proteins are acquired by ribosomal translation of mRNA, it has been theorised that ribosome biogenesis may have an impact on promoting and facilitating recovery after stroke. However, the relationship between stroke and ribosome biogenesis has not been investigated. Methods: In the present study, a ribosome biogenesis gene signature (RSG) was developed using Cox and least absolute shrinkage and selection operator (LASSO) analysis. We classified ischaemic stroke patients into high-risk and low-risk groups using the obtained relevant genes, and further elucidated the immune infiltration of the disease using ssGSEA, which clarified the close relationship between ischaemic stroke and immune subgroups. The concentration of related proteins in the serum of stroke patients was determined by ELISA, and the patients were divided into groups to evaluate the effect of the ribosome biogenesis gene on patients. Through bioinformatics analysis, we identified potential IS-RSGs and explored future therapeutic targets, thereby facilitating the development of more effective therapeutic strategies and novel drugs against potential therapeutic targets in ischaemic stroke. Results: We obtained a set of 12 ribosome biogenesis-related genes (EXOSC5, MRPS11, MRPS7, RNASEL, RPF1, RPS28, C1QBP, GAR1, GRWD1, PELP1, UTP, ERI3), which play a key role in assessing the prognostic risk of ischaemic stroke. Importantly, risk grouping using ribosome biogenesis-related genes was also closely associated with important signaling pathways in stroke. ELISA detected the expression of C1QBP, RPS28 and RNASEL proteins in stroke patients, and the proportion of neutrophils was significantly increased in the high-risk group. Conclusions: The present study demonstrates the involvement of ribosomal biogenesis genes in the pathogenesis of ischaemic stroke, providing novel insights into the underlying pathogenic mechanisms and potential therapeutic strategies for ischaemic stroke.

Leech Poecilobdella manillensis protein extract ameliorated hyperuricemia by restoring gut microbiota dysregulation and affecting serum metabolites.

BACKGROUND: Hyperuricemia (HUA) is a public health concern that needs to be solved urgently. The lyophilized powder of Poecilobdella manillensis has been shown to significantly alleviate HUA; however, its underlying metabolic regulation remains unclear. AIM: To explore the underlying mechanisms of Poecilobdella manillensis in HUA based on modulation of the gut microbiota and host metabolism. METHODS: A mouse model of rapid HUA was established using a high-purine diet and potassium oxonate injections. The mice received oral drugs or saline. Additionally, 16S rRNA sequencing and ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics were performed to identify changes in the microbiome and host metabolome, respectively. The levels of uric acid transporters and epithelial tight junction proteins in the renal and intestinal tissues were analyzed using an enzyme-linked immunosorbent assay. RESULTS: The protein extract of Poecilobdella manillensis lyophilized powder (49 mg/kg) showed an enhanced anti-trioxypurine ability than that of allopurinol (5 mg/kg) (P < 0.05). A total of nine bacterial genera were identified to be closely related to the anti-trioxypurine activity of Poecilobdella manillensis powder, which included the genera of Prevotella, Delftia, Dialister, Akkermansia, Lactococcus, Escherichia_Shigella, Enterococcus, and Bacteroides. Furthermore, 22 metabolites in the serum were found to be closely related to the anti-trioxypurine activity of Poecilobdella manillensis powder, which correlated to the Kyoto Encyclopedia of Genes and Genomes pathways of cysteine and methionine metabolism, sphingolipid metabolism, galactose metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis. Correlation analysis found that changes in the gut microbiota were significantly related to these metabolites. CONCLUSION: The proteins in Poecilobdella manillensis powder were effective for HUA. Mechanistically, they are associated with improvements in gut microbiota dysbiosis and the regulation of sphingolipid and galactose metabolism.

Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma.

Objectives: Combination therapy of lenvatinib and immune checkpoint inhibitors (CLICI) has emerged as a promising approach for managing unresectable hepatocellular carcinoma (HCC). However, the response to such treatment is observed in only a subset of patients, underscoring the pressing need for reliable methods to identify potential responders. Materials & methods: This was a retrospective analysis involving 120 patients with unresectable HCC. They were divided into training (n = 72) and validation (n = 48) cohorts. We developed an interpretable deep learning model using multiphase computed tomography (CT) images to predict whether patients will respond or not to CLICI treatment, based on the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1). We evaluated the models' performance and analyzed the impact of each CT phase. Critical regions influencing predictions were identified and visualized through heatmaps. Results: The multiphase model outperformed the best biphase and uniphase models, achieving an area under the curve (AUC) of 0.802 (95% CI = 0.780-0.824). The portal phase images were found to significantly enhance the model's predictive accuracy. Heatmaps identified six critical features influencing treatment response, offering valuable insights to clinicians. Additionally, we have made this model accessible via a web server at http://uhccnet.com/ for ease of use. Conclusions: The integration of multiphase CT images with deep learning-generated heatmaps for predicting treatment response provides a robust and practical tool for guiding CLICI therapy in patients with unresectable HCC.